iVUN - interactive Visualization of Uncertain biochemical reaction Networks

iVUN is a visual analytics system that supports an uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks (BRNs). These are often described by mathematical models, such as ordinary differential equations (ODEs), which enable the integration of a multitude of different data and data types using parameter estimation. Due to the limited amount of data, parameter estimation does not necessarily yield a single point in parameter space and many attributes of the model remain uncertain.

Our system visualizes the model as a graph, where the statistics of the attributes are mapped to the color of edges and vertices. The graph view is combined with several linked views such as lineplots, scatterplots, and correlation matrices, to support the identification of uncertainties and the analysis of their mutual dependencies as well as their time dependencies.


iVUN is available Open Source on Bioinformatics.org. The official web page of iVUN can be found here. A video demoonstrating the usage of iVUN can be found here.

The tool can be downloaded as executable Jar-file:



iVUN1.1.1.jar (iVUN1.1.0.jar)

A complete tutorial is available as PDF:  

iVUN-1-1-1-Tutorial.pdf (iVUN-1-1-0-Tutorial.pdf)

Supplemental Material

Datasets that have been used for the qualitative user study of the first publication as well as the case study of themore recent publication can be download for test purposes:

Caspase Cascade Eissing model for apoptosis




Dataset used for the case study:

Epo-induced JAK2/STAT5 signaling.

A detailed description of the workflow for the case study, i.e. how all plots have been obtained, can be found in the pdf.





System Overview of iVUN showing the Insulin signaling model. Edges and vertices are colored based on the standard deviation at the steady state (t=30 min) of flux and concentration samples, respectively. The two reactions v7 and v13 as well as the species Xp have been selected and are therefore highlighted. The time courses of fluxes and concentrations can be analyzed within line plots (right), where within the flux line plot the lines for all fluxes besides the two selected reactions are hidden to reduce clutter. The table of mean values and standard deviations for the static parameter samples (bottom left) shows that there are several uncertain parameters; the two selected parameters k7 and k13 are highlighted. The histogram (bottom right) allows for the detailed study of the distribution of individual parameters; here the two selected parameters. The Pearson correlation matrix (left) reveals a strong anti-correlation of k7 and k13 (selected cell within the matrix). The selected species Xp is the species with the highest uncertainty as visible due to color mapping in the graph view and the huge semitransparent area framing the respective line in the line plot.